We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings. In the rearrangement task, the goal is to bring a given physical environment into a specified state. The goal state can be specified by object poses, by images, by a description in language, or by letting the agent experience the environment in the goal state. We characterize rearrangement scenarios along different axes and describe metrics for benchmarking rearrangement performance. To facilitate research and exploration, we present experimental testbeds of rearrangement scenarios in four different simulation environments. We anticipate that other datasets will be released and new simulation platforms will be built to support training of rearrangement agents and their deployment on physical systems.
@article{arxiv.2011.01975,
title = {Rearrangement: A Challenge for Embodied AI},
author = {Dhruv Batra and Angel X. Chang and Sonia Chernova and Andrew J. Davison and Jia Deng and Vladlen Koltun and Sergey Levine and Jitendra Malik and Igor Mordatch and Roozbeh Mottaghi and Manolis Savva and Hao Su},
journal= {arXiv preprint arXiv:2011.01975},
year = {2020}
}